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Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and deploy scalable batch and real-time data pipelines using Python, PySpark, Spark SQL, and Databricks.
Manage and optimize Databricks cloud infrastructure on AWS or GCP including secure integrations and performance tuning using Delta Lake features.
Lead technical best practices, mentoring, automated CI/CD pipeline establishment, and enforce data governance compliance including financial regulations.
Minimum Requirements
Strong proficiency in Python (including pandas, pytest) and advanced SQL (window functions, CTEs, query optimization).
Deep hands-on experience with Apache Spark (PySpark) and at least 3 years developing on Databricks, including Delta Lake ACID transactions and Unity Catalog.
Experience deploying and managing Databricks environments on AWS or GCP with cloud-native components like S3/GCS, IAM, and related services.
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced technical lead able to guide senior and mid-level engineers within Agile/Scrum environments.
Expert in cloud-native data engineering with strong focus on Databricks lakehouse platform and distributed data processing.
Skilled at collaborating cross-functionally with Data Science and BI teams to operationalize ML models and build semantic layers.